This project aims to develop deep learning-based computer-assisted tools to improve perinatal care. Specifically, two different branches are investigated:

1. a) fetal ultrasound – the goal is to develop a method for fetal ultrasound video analysis that is able to automatic fetal biometry measurements and estimates the fetal birth weight at various gestational ages using multimodal data.
2. b) fetoscopic laser surgery – the goal is to develop a real-time method that is able to segment and visualize placental vessels during fetoscopic laser photocoagulation for Twin-to-Twin Transfusion Syndrome (TTTS).